Voice AI has an evaluation problem. Models look strong on public benchmarks, then collapse on real-world audio. Introducing sonar.psdn.ai: a recipe-driven evaluation framework for low-resource languages, real-world audio, and production failure modes. Details ↓

seven days ago we launched Numo with @psdnai. a billion people speak hindi, bengali, tamil, telugu. less than 0.1% of voice ai training data is in any of them (like in microsoft's VibeVoice latest model). nobody was fixing this big training gap, so we started collecting thousands of hours from contributors all over india. week one stats: > 210,000 contributions > 18,000 contributors > contributions growing about 40% day over day every contribution registered as IP. every contributor rewarded. every second of audio licensed at the moment it's create on story. Vietnamese launched just hours ago and we already have thousands of contributions! here's a some fun facts about why we are focusing on Vietnamese: Vietnamese has six tones across three regional dialect systems: Northern, Central, Southern, with big differences in tone systems, vocabulary, and pronunciation Central Vietnamese is the most different and it’s the most challenging dialect, even for other Vietnamese speakers Most voice AI is trained on Northern Vietnamese which misses roughly half of how the country actually speaks thanks to Numo's collection efforts AI models will be able to upskill on conversational nuances and be more realistic. we're also not stopping at voice. we'll start rolling out other forms of tasks soon, always focused on long tail, hard to scrape real world data the models can't easily scrape from the internet.